{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([11, 1])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "torch.set_printoptions(edgeitems=2, linewidth=75)\n",
    "\n",
    "\n",
    "t_c = [0.5,  14.0, 15.0, 28.0, 11.0,  8.0,  3.0, -4.0,  6.0, 13.0, 21.0]\n",
    "t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]\n",
    "t_c = torch.tensor(t_c).unsqueeze(1) # <1>\n",
    "t_u = torch.tensor(t_u).unsqueeze(1) # <1>\n",
    "\n",
    "t_u.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 9,  6,  3,  8,  0, 10,  7,  5,  2]), tensor([1, 4]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_samples = t_u.shape[0]\n",
    "n_val = int(0.2 * n_samples)\n",
    "\n",
    "shuffled_indices = torch.randperm(n_samples)\n",
    "\n",
    "train_indices = shuffled_indices[:-n_val]\n",
    "val_indices = shuffled_indices[-n_val:]\n",
    "\n",
    "train_indices, val_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_u_train = t_u[train_indices]\n",
    "t_c_train = t_c[train_indices]\n",
    "\n",
    "t_u_val = t_u[val_indices]\n",
    "t_c_val = t_c[val_indices]\n",
    "\n",
    "t_un_train = 0.1 * t_u_train\n",
    "t_un_val = 0.1 * t_u_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9, 1])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_u_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "# linear_model = nn.Linear(1,1)\n",
    "# linear_model(t_un_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.weight.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[0.9181]], requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([0.3509], requires_grad=True)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(linear_model.parameters())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(linear_model.parameters(),lr = 1e-2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用nn时的循环\n",
    "def traing_loop(n_epoch,optimizer,model,loss_fn,t_u_train,t_u_val,t_c_train,t_c_val):\n",
    "    for epoch in range(1,n_epoch+1):\n",
    "        t_p_train = model(t_u_train)\n",
    "        loss_train = loss_fn(t_p_train,t_c_train)\n",
    "        \n",
    "        t_p_val = model(t_u_val)\n",
    "        loss_val = loss_fn(t_p_val,t_c_val)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss_train.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if epoch == 1 or epoch % 1000 ==0:\n",
    "            print(f\"Epoch {epoch},Training loss {loss_train.item():.4f},\"\n",
    "                  f\"Validation loss {loss_val.item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1,Training loss 53.4151,Validation loss 204.2742\n",
      "Epoch 1000,Training loss 4.2152,Validation loss 17.4395\n",
      "Epoch 2000,Training loss 3.0067,Validation loss 7.6639\n",
      "Epoch 3000,Training loss 2.9182,Validation loss 5.7012\n",
      "\n",
      "Parameter containing:\n",
      "tensor([[4.9297]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([-15.0811], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "linear_model = nn.Linear(1,1)                           # 引用模型时也初始化了参数\n",
    "optimizer = optim.SGD(linear_model.parameters(),lr = 1e-2)\n",
    "\n",
    "traing_loop(n_epoch=3000,optimizer=optimizer,\n",
    "           model = linear_model,loss_fn=nn.MSELoss(),\n",
    "           t_u_train=t_un_train,t_u_val=t_un_val,\n",
    "           t_c_train=t_c_train,t_c_val=t_c_val)\n",
    "\n",
    "print()\n",
    "print(linear_model.weight)\n",
    "print(linear_model.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将线性模型替换为神经网络模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=13, bias=True)\n",
       "  (1): Tanh()\n",
       "  (2): Linear(in_features=13, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_model = nn.Sequential(nn.Linear(1,13),nn.Tanh(),nn.Linear(13,1))\n",
    "\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[torch.Size([13, 1]), torch.Size([13]), torch.Size([1, 13]), torch.Size([1])]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看一下网络中的参数\n",
    "[param.shape for param in seq_model.parameters()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.weight torch.Size([13, 1])\n",
      "0.bias torch.Size([13])\n",
      "2.weight torch.Size([1, 13])\n",
      "2.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "#通过显示序号的方式\n",
    "\n",
    "for name,param in seq_model.named_parameters():\n",
    "    print(name,param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (hidden_linear): Linear(in_features=1, out_features=8, bias=True)\n",
       "  (hidden_activation): Tanh()\n",
       "  (output_linear): Linear(in_features=8, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为每一层添加name\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "seq_model =nn.Sequential(OrderedDict([\n",
    "    ('hidden_linear',nn.Linear(1,8)),\n",
    "    ('hidden_activation',nn.Tanh()),\n",
    "    ('output_linear',nn.Linear(8,1))\n",
    "]))\n",
    "\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden_linear.weight torch.Size([8, 1])\n",
      "hidden_linear.bias torch.Size([8])\n",
      "output_linear.weight torch.Size([1, 8])\n",
      "output_linear.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "for name,param in seq_model.named_parameters():\n",
    "    print(name,param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1,Training loss 190.4733,Validation loss 154.1533\n",
      "Epoch 1000,Training loss 4.4755,Validation loss 9.4510\n",
      "Epoch 2000,Training loss 2.2093,Validation loss 5.9661\n",
      "Epoch 3000,Training loss 1.7019,Validation loss 3.7018\n",
      "Epoch 4000,Training loss 1.4839,Validation loss 2.9160\n",
      "Epoch 5000,Training loss 1.3770,Validation loss 2.6897\n",
      "ouput tensor([[13.0862],\n",
      "        [13.2522]], grad_fn=<AddmmBackward>)\n",
      "answer tensor([[14.],\n",
      "        [11.]])\n",
      "hidden tensor([[ 4.7889e+00],\n",
      "        [ 5.2126e+00],\n",
      "        [-2.6709e-01],\n",
      "        [-2.5762e-01],\n",
      "        [ 5.9991e+00],\n",
      "        [ 7.8793e-05],\n",
      "        [ 1.9324e-01],\n",
      "        [ 5.4420e+00]])\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(seq_model.parameters(),lr = 1e-2)\n",
    "\n",
    "traing_loop(n_epoch=5000,optimizer=optimizer,\n",
    "           model = seq_model,loss_fn=nn.MSELoss(),\n",
    "           t_u_train=t_un_train,t_u_val=t_un_val,\n",
    "           t_c_train=t_c_train,t_c_val=t_c_val)\n",
    "\n",
    "print('ouput',seq_model(t_un_val))\n",
    "print('answer',t_c_val)\n",
    "print('hidden',seq_model.hidden_linear.weight.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 与线性模型比较\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (70, 1) and (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-a45547ef1bd5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'摄氏'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_u\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_c\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseq_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt_range\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'c-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2794\u001b[0m     return gca().plot(\n\u001b[1;32m   2795\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2796\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[1;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (70, 1) and (1,)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 21326 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 27663 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 21326 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 27663 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 25668 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/lzhao/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 25668 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_range = torch.arange(20.,90.).unsqueeze(1)\n",
    "\n",
    "fig = plt.figure(dpi=600)\n",
    "plt.xlabel('华氏')\n",
    "plt.ylabel('摄氏')\n",
    "plt.plot(t_u.numpy(),t_c.numpy(),'o')\n",
    "plt.plot(t_range.numpy(),seq_model(0.1*t_range).detach().numpy,'c-')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.xlabel(\"Fahrenheit\")\n",
    "plt.ylabel(\"Celsius\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.plot(t_range.numpy(), seq_model(0.1 * t_range).detach().numpy(), 'c-')\n",
    "plt.plot(t_u.numpy(), seq_model(0.1 * t_u).detach().numpy(), 'kx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
